Samar
5 years & 5 months ago

hello ,
The above part is very helpful.Please do let me know how to perform ARIMAX in Stata and how to do the prediction part in ARIMAX and how many X variables can be used in ARIMAX ?

Sayed Alim Samim
3 years & 1 month ago

Hi, dear sir
I need, help in time series analysis, I want to do some forecasting.

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Simon Turner
5 years & 5 months ago

This is super helpful!
One question I have is in extracting the data from Stata in my do file?
I can find the value of, eg. AR(1) in the returned e(b), but can’t find the standard error or confidence interval in any of the returned lists (although they’re displayed on the screen I’d like to store the values in a local macro).
Is there a way to do this?
Thanks!

Saptarshi Basu Roy Choudhury
5 years & 5 months ago

Hello Simon,

Thanks that you found the article helpful. I would like to share that you can also use commands for ARIMA modeling in STATA instead of using menus from drop down lists as shown in this article. That way you will be able to store all your commands in a do file and just run the commands next time to get all the results. The command you can use for ARIMA modeling is “arima depvar, arima (orders separated by commas)”. For example, in this case, the command will be “arima GDP, arima (9,2,1)” to get the results for ARIMA (9,2,1) model.

Coming to your query, if you use e(b) command, it will return just the coefficient values as you pointed out because e(b) is used to produce a matrix of coefficients. That’s why it does not return the standard errors or the confidence intervals. So, instead of using e(b), you can use either of these methods to store your results in a text file or a word file.

First, run the arima model. Then use these commands, “translate @Results (name of the text file).txt” followed by “type (name of the text file).txt”. It will save all the results displayed in the results window in the designated text file.

Alternatively, first run the arima model. Then use “outreg2” command to export the output to a word file. This will return the standard errors within parentheses below the coefficient values. To use it, first you need to install the command by writing “ssc install outreg2” in the command window. Then use the command “outreg2 (name of the word file).doc” to export the output.

Or you can just highlight the results (including std errors and confidence intervals) you need to store, copy them and paste them in an excel file, create a table and then export them to a word file.

Hope this helps. Let us know.

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Sudeshna
5 years & 9 months ago

Dear Colleen,

Thanks for sharing your concern!
For interrupted time series analysis (ITSA), it’s better to use OLS over ARIMA, as the former is more flexible and broadly applicable in an interrupted time-series context.

The syntax (code) to use for itsa is:

itsa depvar [indepvars] [if] [in] [weight], trperiod(numlist) [ single treatid(#) contid(numlist) prais lag(#) figure posttrend replace prefix(string) model_options ]

Note, this code is data specific but I have presented the full form of the common codes below:

trperiod(numlist) specifies the time period when the intervention begins. The values entered for time period must be in the same units as the panel time variable specified in tsset timevar; see [TS] tsset. More than one period may be specified.
trperiod() is required.

single indicates that itsa will be used for a single group analysis. Conversely, omitting single indicates that itsa is for a multiple group comparison.

treatid(#) specifies the identifier of the single treated unit under study when the dataset contains multiple panels. The value entered must be in the same units as the panel variable specified in tsset panelvar timevar; see [TS] tsset. When the dataset contains data for only a single panel, treatid() must be omitted.

contid(numlist) specifies a list of identifiers to be used as control units in the multiple group analysis. The values entered must be in the same units as the panel variable specified in tsset panelvar timevar; see [TS] tsset. If contid() is not specified, all non-treated units in the data will be used as controls.

prais specifies that a prais model should be estimated. If prais is not specified, itsa will use newey as the default model.

lag(#) specifies the maximum lag to be considered in the autocorrelation structure when a newey model is chosen. If the user specifies lag(0), the output is the same as regress, vce(robust); Default is lag(0). An error message will appear if both prais and lag() are specified, as prais implements an AR(1) model, by design.

figure produces a line plot of the predicted depvar variable combined with a scatter plot of the actual values of depvar over time. In a multiple group analysis, figure plots the average values of all controls used in the analysis (more specifically, data for specified controls are collapsed and the monthly observations averaged).

posttrend produces post-treatment trend estimates using lincom, for the specified model. In the case of a single-group ITSA, one estimate is produced. In the case of a multiple-group ITSA, an estimate is produced for the treatment group, the control
group, and the difference. In the case of multiple treatment periods, a separate table is produced for each treatment period.
replace replaces variables created by itsa if they already exist. If prefix() is specified, only variables created by itsa with the same prefix will be replaced.

prefix(string) adds a prefix to the names of variables created by itsa. Short prefixes are recommended.

model_options specifies all available options for prais when the prais option is chosen; otherwise all available options of newey other than lag().

Hope the above notes help. Do let us know!

Colleen
5 years & 9 months ago

Hello,

Thank you for this information. I have found the ARIMA model for the time series I will be analyzing. I am not sure, however, how to perform the interrupted time series analysis in stata using these ARIMA models. I am not sure what commands I would use to perform this analysis in STATA with the ARIMA models.

Thank you.

Dejan
6 years & 1 week ago

All the discussions you have posted on time series analysis in STATA are excellent, brief and applicable. Thank You Very Much.

Priya Chetty
6 years & 6 days ago

Dear Dejan,
Greetings! Thanks for the compliment! We are happy to help 🙂

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